Frankhihi/goemotion-ekman-emotions

GoEmotions Ekman Emotions Dataset

Dataset Description

This dataset contains 10,000 text samples from Reddit comments mapped to the 7 basic Ekman emotions. It's derived from the original GoEmotions dataset and processed specifically for emotion classification research using Paul Ekman's fundamental emotion model.

Supported Tasks

• Text Classification: Multi-class emotion classification
• Sentiment Analysis: Fine-grained emotion detection
• Psychology Research: Emotion analysis in social media text

Dataset Statistics

Class Distribution

• Joy: 3,053 samples (30.5%)
• Neutral: 1,962 samples (19.6%)
• Anger: 1,529 samples (15.3%)
• Fear: 1,024 samples (10.2%)
• Sadness: 1,005 samples (10.1%)
• Surprise: 925 samples (9.2%)
• Disgust: 502 samples (5.0%)

Key Features

• Domain: Social media (Reddit comments)
• Language: English
• Emotion Model: Ekman's 7 basic emotions
• Quality: Preprocessed and cleaned text data
• Balance: Natural distribution reflecting real-world emotion occurrence

Dataset Structure

Data Fields

• text (string): The Reddit comment text
• ekman_emotion (string): One of 7 emotions: anger, disgust, fear, joy, neutral, sadness, surprise

Example

{
  "text": "I absolutely love this new feature! It makes everything so much easier.",
  "ekman_emotion": "joy"
}

Usage

Load with Datasets Library

from datasets import load_dataset

# Load the full dataset
dataset = load_dataset("goemotion-ekman-emotions")

# Load specific splits
train_dataset = load_dataset("goemotion-ekman-emotions", split="train")

# Preview the data
print(dataset["train"][0])

Training Example

from datasets import load_dataset
from transformers import AutoTokenizer, AutoModelForSequenceClassification
from transformers import TrainingArguments, Trainer

# Load dataset
dataset = load_dataset("goemotion-ekman-emotions")

# Load tokenizer and model  
tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased")
model = AutoModelForSequenceClassification.from_pretrained(
    "distilbert-base-uncased", 
    num_labels=7
)

# Tokenize function
def tokenize_function(examples):
    return tokenizer(examples["text"], truncation=True, padding="max_length")

# Tokenize dataset
tokenized_dataset = dataset.map(tokenize_function, batched=True)

# Train model (example)
training_args = TrainingArguments(
    output_dir="./emotion-classifier",
    num_train_epochs=3,
    per_device_train_batch_size=16,
    evaluation_strategy="epoch",
)

trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=tokenized_dataset["train"],
    tokenizer=tokenizer,
)

trainer.train()

Emotion Labels

The dataset uses Ekman's 7 basic emotions:

1. Anger: Irritation, annoyance, rage, fury
2. Disgust: Revulsion, distaste, repugnance
3. Fear: Anxiety, worry, nervousness, terror
4. Joy: Happiness, delight, amusement, love, excitement
5. Neutral: No strong emotion expressed
6. Sadness: Sorrow, grief, disappointment, melancholy
7. Surprise: Astonishment, amazement, wonder

Preprocessing

The dataset has been:

• ✅ Cleaned: Removed invalid entries and duplicates
• ✅ Normalized: Consistent text formatting
• ✅ Mapped: Original 27 GoEmotions labels mapped to 7 Ekman emotions
• ✅ Validated: Quality checked for label accuracy

Citation

If you use this dataset, please cite the original GoEmotions paper:

@inproceedings{demszky2020goemotions,
  title={GoEmotions: A Dataset of Fine-Grained Emotions},
  author={Demszky, Dorottya and Movshovitz-Attias, Dana and Ko, Jeongwoo and Cowen, Alan and Nemade, Gaurav and Ravi, Sujith},
  booktitle={Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics},
  pages={4040--4054},
  year={2020}
}

License

This dataset is released under the Apache 2.0 License.

Dataset Card Contact

For questions about this dataset, please open an issue in the repository or contact the dataset maintainer.

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Dataset curated for emotion classification research • Compatible with Hugging Face Transformers • Ready for training and evaluation